MRI-based quantification of intratumoral heterogeneity for intrahepatic mass-forming cholangiocarcinoma grading: a multicenter study

医学 接收机工作特性 神经组阅片室 分级(工程) 放射科 无线电技术 肝内胆管癌 磁共振成像 核医学 内科学 神经学 土木工程 精神科 工程类
作者
Liyong Zhuo,Wenjing Chen,Li-Hong Xing,Xiaomeng Li,Zijun Song,Jinghui Dong,Yanyan Zhang,Hongjun Li,Jingjing Cui,Yuxiao Han,Jiawei Hao,Jianing Wang,Xiaoping Yin,Caiying Li
出处
期刊:Insights Into Imaging [Springer Nature]
卷期号:16 (1)
标识
DOI:10.1186/s13244-025-01985-9
摘要

Abstract Objective This study aimed to develop a quantitative approach to measure intratumor heterogeneity (ITH) using MRI scans and predict the pathological grading of intrahepatic mass-forming cholangiocarcinoma (IMCC). Methods Preoperative MRI scans from IMCC patients were retrospectively obtained from five academic medical centers, covering the period from March 2018 to April 2024. Radiomic features were extracted from the whole tumor and its subregions, which were segmented using K-means clustering. An ITH index was derived from a habitat model integrating output probabilities of the subregions-based models. Significant variables from clinical laboratory-imaging features, radiomics, and the habitat model were integrated into a predictive model, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results The final training and internal validation datasets included 197 patients (median age, 59 years [IQR, 52–65 years]); the external validation dataset included 43 patients (median age, 58.5 years [IQR, 52.25–69.75 years]). The habitat model achieved AUCs of 0.847 (95% CI: 0.783, 0.911) in the training set and 0.753 (95% CI: 0.595, 0.911) in the internal validation set. Furthermore, the combined model, integrating imaging variables, the habitat model, and radiomics model, demonstrated improved predictive performance, with AUCs of 0.895 (95% CI: 0.845, 0.944) in the training dataset, 0.790 (95% CI: 0.65, 0.931) in the internal validation dataset, and 0.815 (95% CI: 0.68, 0.951) in the external validation dataset. Conclusion The combined model based on MRI-derived quantification of ITH, along with clinical, laboratory, radiological, and radiomic features, showed good performance in predicting IMCC grading. Critical relevance statement This model, integrating MRI-derived intrahepatic mass-forming cholangiocarcinoma (IMCC) classification metrics with quantitative radiomic analysis of intratumor heterogeneity (ITH), demonstrates enhanced accuracy in tumor grade prediction, advancing risk stratification for clinical decision-making in IMCC management. Key Points Grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) is important for risk stratification, clinical decision-making, and personalized therapeutic optimization. Quantitative intratumor heterogeneity can accurately predict the pathological grading of IMCC. This combined model provides higher diagnostic accuracy. Graphical Abstract
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